Adaptive neural control for a class of stochastic nonlinear pure-feedback systems with unknown control direction

Author

Yu Zhaoxu ; Luo Jianxu ; Du Hongbin

Author_Institution

Dept. of Autom., East China Univ. of Sci. & Technol., Shanghai, China

fYear

2011

fDate

22-24 July 2011

Firstpage

682

Lastpage

687

Abstract

This paper addresses a class of uncertain stochastic nonlinear pure-feedback systems with unknown control direction. With using the decoupled backstepping technique, adaptive neural control schemes are designed to solve the stabilization problem of such systems. Stability analysis is presented to guarantee that all the error variables are semi-globally ultimately bounded with desired probability in a compact set. The effectiveness of the proposed design is verified by simulation results.

Keywords

adaptive control; control system synthesis; feedback; neurocontrollers; nonlinear systems; stability; stochastic systems; uncertain systems; adaptive neural control; compact set; decoupled backstepping technique; stability analysis; stabilization problem; uncertain stochastic nonlinear pure-feedback system; unknown control direction; Adaptive systems; Artificial neural networks; Backstepping; Control design; Nonlinear systems; Stability analysis; Adaptive Control; Backstepping; Neural Networks (NN); Nussbaum Gain Functions (NGFs); Stochastic Pure-Feedback Systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2011 30th Chinese

Conference_Location

Yantai

ISSN

1934-1768

Print_ISBN

978-1-4577-0677-6

Electronic_ISBN

1934-1768

Type

conf

Filename

6001312